[1O1-GS-7] Vision, speech media processing: GAN

[1O1-GS-7-03] Generation Based on Non-verbal Impression by Conditional FastGAN and Color Psychological Effects

〇Komei Hiruta¹, Ryusuke Saito¹, Taro Hatakeyama¹, Atsushi Hashimoto^1,2, Satoshi Kurihara¹ (1. Keio University, 2. OMRON SINIC X Corp)

Keywords:Deep Generative Model, Conditonal GAN, Impressional Feature Space, Color Psychology, Manga

One of the requirements for creating engaging content such as comics and games is to design fascinating characters. Fascinating characters may come out by intuitive inspiration that cannot be expressed in words. On the other hand, excellent inspiration, which is the source of creative ideas, is not something that people can just come up with. Therefore, the purpose of this research is to help creators expand their imagination by controlling the generated images based on non-verbal impressions. First, we propose Conditional FastGAN, which can generate high-quality data even on small datasets such as artworks. In addition, in order to extract the "impressions" that people receive from images as features, we have developed an annotation system that utilizes "color" as a non-verbal impression medium. Our experiments, used cartoon face images extracted from Osamu Tezuka's works, the MUCT dataset consisting of face photographs in various orientations, and a color-based impression information dataset obtained by our system. The results showed the possibility to generate images corresponding to the conditions of live images, cartoons, and impressions.